An image classification method for arrhythmias based on Gramian angular summation field and improved Inception-ResNet-v2.
10.7507/1001-5515.202207049
- Author:
Xiangkui WAN
1
;
Jing LUO
1
;
Yang LIU
1
;
Yunfan CHEN
1
;
Xingwei PENG
2
;
Xi WANG
3
Author Information
1. Hubei Key Laboratory for High-efficiency Utilization of Solar Energy and Operation Control of Energy Storage System, Hubei University of Technology, Wuhan 430068, P. R. China.
2. Wuhan Sichuang Electronics Co., Ltd., Wuhan 430074, P. R. China.
3. Department of Cardiology, Renmin Hospital of Wuhan University, Wuhan 430060, P. R. China.
- Publication Type:Journal Article
- Keywords:
Deep convolutional generative adversarial network;
Gramian angular summation field;
Image classification of arrhythmias;
Inception-ResNet-v2
- MeSH:
Humans;
Arrhythmias, Cardiac/diagnostic imaging*;
Cardiovascular Diseases;
Algorithms;
Databases, Factual;
Electrocardiography
- From:
Journal of Biomedical Engineering
2023;40(3):465-473
- CountryChina
- Language:Chinese
-
Abstract:
Arrhythmia is a significant cardiovascular disease that poses a threat to human health, and its primary diagnosis relies on electrocardiogram (ECG). Implementing computer technology to achieve automatic classification of arrhythmia can effectively avoid human error, improve diagnostic efficiency, and reduce costs. However, most automatic arrhythmia classification algorithms focus on one-dimensional temporal signals, which lack robustness. Therefore, this study proposed an arrhythmia image classification method based on Gramian angular summation field (GASF) and an improved Inception-ResNet-v2 network. Firstly, the data was preprocessed using variational mode decomposition, and data augmentation was performed using a deep convolutional generative adversarial network. Then, GASF was used to transform one-dimensional ECG signals into two-dimensional images, and an improved Inception-ResNet-v2 network was utilized to implement the five arrhythmia classifications recommended by the AAMI (N, V, S, F, and Q). The experimental results on the MIT-BIH Arrhythmia Database showed that the proposed method achieved an overall classification accuracy of 99.52% and 95.48% under the intra-patient and inter-patient paradigms, respectively. The arrhythmia classification performance of the improved Inception-ResNet-v2 network in this study outperforms other methods, providing a new approach for deep learning-based automatic arrhythmia classification.
